Function reference • bayesdfa

Using the bayesdfa package Functions for fitting Bayesian dynamic factor analyses
`fit_dfa()`	Fit a Bayesian DFA
`sim_dfa()`	Simulate from a DFA
`find_dfa_trends()`	Find the best number of trends according to LOOIC
`dfa_cv()`	Apply cross validation to DFA model
Diagnostics for fitted models Functions for evaluating convergence of DFA moddels
`find_swans()`	Find outlying "black swan" jumps in trends
`is_converged()`	Summarize Rhat convergence statistics across parameters
`loo(<bayesdfa>)`	LOO information criteria
`predicted()`	Calculate predicted value from DFA object
`rotate_trends()`	Rotate the trends from a DFA
`trend_cor()`	Estimate the correlation between a DFA trend and some other timeseries
Extracting DFA output Functions for extracting common outputs
`dfa_fitted()`	Get the fitted values from a DFA as a data frame
`dfa_loadings()`	Get the loadings from a DFA as a data frame
`dfa_trends()`	Get the trends from a DFA as a data frame
Univariate Hidden Markov Models Functions for evaluating regimes with univariate HMMs
`hmm_init()`	Create initial values for the HMM model.
`find_regimes()`	Fit multiple models with differing numbers of regimes to trend data
`fit_regimes()`	Fit models with differing numbers of regimes to trend data
Plotting Functions for plotting DFA and HMM models
`plot_fitted()`	Plot the fitted values from a DFA
`plot_loadings()`	Plot the loadings from a DFA
`plot_regime_model()`	Plot the state probabilities from `find_regimes()`
`plot_trends()`	Plot the trends from a DFA

Reference